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The North Atlantic The NAO, AO and the MJO Hai Lin Meteorological Research Division, Environment Canada Workshop Sub-seasonal to Seasonal Prediction Met.

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Presentation on theme: "The North Atlantic The NAO, AO and the MJO Hai Lin Meteorological Research Division, Environment Canada Workshop Sub-seasonal to Seasonal Prediction Met."— Presentation transcript:

1 The North Atlantic The NAO, AO and the MJO Hai Lin Meteorological Research Division, Environment Canada Workshop Sub-seasonal to Seasonal Prediction Met Office, Exeter, Dec. 1-3, 2010

2 Outlines Challenge of prediction in the North Atlantic and Europe Brief introduction of NAO / AO and its impact NAO prediction on intraseasonal time scale MJO contribution; intraseasonal hindcast NAO seasonal prediction possible signal sources; skill in four Canadian AGCMs

3 Challenge of subseasonal and seasonal prediction in the North Atlantic and European region Strong variability due to atmospheric internal nonlinear interactions Far from major source of interannual variability (e.g., ENSO) Low forecast skill

4 What is the NAO? The North Atlantic Oscillation is a large-scale seesaw in atmospheric mass between the subtropical high-pressure system over the Azores Islands and the subpolar low-pressure system over Iceland. (From American Museum of Natural History website)

5 The NAO The NAO is one of the most important modes of atmospheric variability in the northern hemisphere The NAO has a larger amplitude in winter than in summer The NAO accounts for 31% of the variance in winter surface air temperature north of 20°N (Hurrell, 1995)

6 The AO The Arctic Oscillation has a global scale, more zonally symmetric, also called the Northern Annular Mode (NAM) Connection to stratosphere (e.g., Baldwin and Dunkerton 2001) The NAO can be regarded as a local representation of the AO in the North Atlantic

7 Impact of the NAO Subtropical high pressure and Icelandic low Westerly winds and storm activity across the Atlantic Ocean Temperature and precipitation in Europe, northeastern Canada and Greenland Impact on forecast skill

8 Causes within the atmosphere: interactions among different scales and frequencies in the atmosphere lack of forecast skill beyond 2 weeks Causes external to the atmosphere (on seasonal and interannual time scales): Sea surface temperature (SST) anomaly in the North Atlantic Changes in ice and snow cover SST anomaly in the tropics How is the NAO variability generated?

9 NAO forecasts Intraseasonal time scale impact of the MJO

10 The Madden-Julian Oscillation (MJO) Discovered by Madden and Julian (1971). Spectrum analysis of 10 year record of SLP at Canton, and upper level zonal wind at Singapore. Peak at 40-50 days. Dominant tropical wave on intraseasonal time scale 30-60 day period, wavenumber 1~3 propagates eastward along the equator (~5 m/s in eastern Hemisphere, and ~10 m/s in western Hemisphere ) Organizes convection and precipitation

11 Composites of tropical Precipitation rate for 8 MJO phases, according to Wheeler and Hendon index. Xie and Arkin pentad data, 1979-2003

12 Connection between the MJO and NAO NAO index: pentad average MJO RMMs: pentad average Period: 1979-2003 Extended winter, November to April (36 pentads each winter)

13 Lagged probability of the NAO index Positive: upper tercile; Negative: low tercile Phase12345678 Lag 5 35%40%+49% Lag 4 +52%+46% Lag 3 40%+46% Lag 2 +50% Lag 1 Lag 0 +45%42% Lag +1 +47%+45%46% Lag +2 +47%+50%+42%41% 42% Lag +3 +48%41%48% Lag +4 39%48% Lag +5 41% (Lin et al. 2009)

14 Tropical influence (Lin et al. JCLIM, 2009) Z500 anomaly

15 Impact on Canadian surface air temperature Lagged winter SAT anomaly in Canada (Lin et al. MWR, 2009)

16 To demonstrate this: Primitive equation GCM (T31, L10) Linear integration, winter basic state with a single center heating source Heating at different longitudes along the equator from 60E to 150W at a 10 degree interval, 16 experiments Z500 response at day 10 Why the response to a dipole heating is the strongest ? Barotropic instability of 2-D basic flow: similar mechanism as Simmons et al. (1983) Rossby wave generation determined by relative position of tropical forcing wrt jet stream (Lin 2010)

17 Day 10 Z500 linear response a) 80E b) 110E c) 150E Similar pattern for heating 60-100E Similar pattern for heating 120-150W Lin et al. (2010)

18 ISO hindscast with GEM GEM clim of Canadian Meteorological Centre (CMC)-- GEMCLIM 3.2.2, 50 vertical levels and 2 o of horizontal resolution 1985-2008 3 times a month (1 st, 11 th and 21 st ) 10-member ensemble (balanced perturbation to NCEP reanalysis) NCEP SST, SMIP and CMC Sea ice, Snow cover: Dewey- Heim (Steve Lambert) and CMC 45-day integrations

19 NAO forecast skill extended winter – Nov – March tropical influence A simple measure of skill: temporal correlation of NAO index btw forecast and observations

20 (Lin et al. GRL, 2010)

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23 Correlation skill: averaged for pentads 3 and 4

24 (Lin et al. GRL, 2010)

25 NAO seasonal forecasts Possible signal sources: Sea surface temperature (SST) anomaly in the North Atlantic (e.g., Rodwell et al. 1999) Changes in ice and snow cover (e.g., Cohen and Entekhabi 1999) SST anomaly in the tropics (e.g., Jia et al. 2008)

26 Historical forecast (HFP2) 4 global models GEM: 2°x2°, 50 levels AGCM2: 625 km (T32), 10 levels AGCM3: 315 km (T63), 32 levels SEF: 210 km (T95), 27 levels Once a month (beginning of each month) 4-month integrations 10 members each model Persistent SST anomaly Sea ice and snow cover anomalies relaxed to climatology 1969-2003

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29 NAO seasonal forecast skill Lead=0: skill in late winter to spring Four models have similar performance Lead=1 month: no skill Possible explanation: – skill comes from initial condition – models do not have a correct response pattern in the NAO (this will be explored in the next couple of slides)

30 Identify dominant forced patterns For the DJFM run: SVD analysis between November tropical Pacific SST and DJF or JFM ensemble mean Z500 The expansion coefficient of SVD2 (Z500) is significantly correlated with the observed NAO index

31 November SST vs JFM z500 Leading pairs of SVD in observations SST Z500

32 November SST vs JFM z500 Leading pairs of SVD in GEM ensemble mean

33 November SST vs JFM z500 Leading pairs of SVD in GCM3 ensemble mean

34 NAO skill of ensemble forecast Forecast NAO indexForced SVD2 GCM2 -0.13 0.30 GCM3 0.26 0.57 SEF 0.33 0.47 GEM 0.25 0.39 Temporal correlation with DJF observed NAO index Lead = 0

35 NAO skill of ensemble forecast Forecast NAO indexForced SVD2 GCM2 -0.31 0.35 GCM3 0.27 0.43 SEF 0.12 0.42 GEM 0.20 0.31 Temporal correlation with JFM observed NAO index Lead = 1 month

36 NAO skill of ensemble forecast Model has a biased NAO pattern The forced SVD2 pattern has a time evolution that matches well the observed NAO index can be used as a skillful forecast of the NAO index

37 Summary Significant impact of the MJO on the NAO NAO intraseasonal forecast skill influenced by the MJO Some skillful NAO seasonal forecast possible in late winter and spring Seasonal forecast of NAO has biased spatial pattern, some statistical post-processing procedure can improve the skill

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